The best Affinda alternative depends on which Affinda product you are replacing. In 2026 Affinda sells two things for documents: Affinda Extract (formerly Affinda Platform), an end-to-end document automation platform with splitting, classification, validation, human review and ERP integrations, and the Affinda Resume Parser, an API for recruitment software. If you want a self-serve extraction API with published prices, look at PDF Vector or Mindee. For finance documents, Veryfi or Nanonets. For enterprise cloud OCR, Azure Document Intelligence. For resume parsing with a choice of engines, Eden AI. This guide compares them with facts checked in November 2026 and says where Affinda is still the better choice.
What Affinda offers today
Affinda has repositioned over the past year. Its own pages (checked November 2026) describe:
- Affinda Extract: a platform that covers ingestion, splitting, classification, extraction, validation and integration. Every extracted value links back to source evidence; failed validation rules route documents to a review UI that can be embedded in your own application; data can be matched against ERP, CRM or master data. Configuration happens in conversation with the "Affinda Extract Agent". See affinda.com/extract.
- Integrations: an API with client libraries, webhooks, email upload and a long list of named destinations, including NetSuite, SAP Business One, Xero, Salesforce, Power Automate, n8n and Pipedream (integrations page).
- Security and deployment: ISO 27001:2022, SOC 2 Type 2 and GDPR compliance, with a HIPAA-compliant deployment available, and multi-tenant SaaS, single-tenant, customer VPC or self-hosted options.
- Pricing: usage-based per page with no seats or feature tiers. A two-week trial includes 200 credits; after that you choose monthly billing in arrears or an annual commitment, and both are quoted rather than published (pricing page).
- Resume Parser: accepts PDF, DOC, DOCX, TXT, RTF, HTML, images, ODT and Excel files, covers 50+ languages, maps job titles to occupation codes and normalizes skills to taxonomies. It is offered hosted or self-hosted, with published prices: US$0.10 per document self-serve (the first 14 days include 1,000 free documents), annual hosted plans from US$3,600 for 66,000 documents, and self-hosted plans from US$12,000 a year for 500,000 documents (resume parser, pricing).
That is a strong product for regulated operations teams. It is also why people look elsewhere: no published price for Affinda Extract, a sales conversation before production, and a platform that is heavier than a developer who "just needs JSON from PDFs" wants.
Why teams look for Affinda alternatives
The common reasons:
- Pricing you can see. For document extraction beyond resumes, teams that want to estimate cost before talking to sales, or run small projects without a commitment, prefer a public price list.
- API-first workflow. Some teams want extraction defined in code (a schema in the request, versioned in git) rather than configured in a vendor's workspace.
- Scope. A full intake-to-ERP platform is more than a SaaS product needs when it only has to read uploaded files.
- Resume parsing only. Recruitment software that only needs CV parsing may want to compare Affinda's parser with other resume engines on price and quality.
- Other document types. Teams that also need document Q&A, Markdown for LLM pipelines or academic papers may want one API for all of it.
Affinda alternatives at a glance
| Tool | Type | Best for | Public pricing (checked November 2026) |
|---|---|---|---|
| PDF Vector | Developer API, SDK, MCP server, Zapier/Make/n8n actions | Schema-based extraction from any document, plus invoice, ID, bank statement and academic APIs | Free 100 credits/month; Basic $25/month (3,000 credits); Pro $97/month (100,000 credits) |
| Mindee | Extraction API with prebuilt and custom models | Developers who want prebuilt invoice, receipt and ID models plus custom extractors | Starter from $53/month billed annually; 1 page = 1 credit; 14-day trial |
| Veryfi | Finance document API and capture SDKs | Receipts, invoices, checks, bank statements, W-2s and W-9s | Free up to 100 docs/month; Starter $500/month minimum |
| Nanonets | Workflow app and extraction API | AP teams that need approvals, review queues and ERP exports | $50 of credits on Starter; $0.02 to $0.30 per block run |
| Azure Document Intelligence | Cloud API with prebuilt and trainable models | Enterprises on Azure; on-premises containers | 500 pages/month free; prebuilt $10 per 1,000 pages |
| Eden AI | AI gateway that routes to many providers | Comparing resume or invoice parsers (including Affinda's) behind one API | Provider price + 5.5% platform fee, no subscription |
1. PDF Vector: schema-based extraction with published credits
PDF Vector is a document and academic API. Instead of configuring document types in a workspace, you send a prompt and a JSON Schema with each request, and Document Extract returns data in that shape. The same pattern works for purchase orders, contracts, application forms or resumes. For high-volume finance and onboarding documents there are dedicated endpoints: Invoice Extract, Identity Extract and bank statement extraction, each with Parse (Markdown) and Ask (a plain-language answer) alongside.
Here is a resume extraction with Python and requests (there is no official Python SDK):
import requests
schema = {
"type": "object",
"properties": {
"fullName": {"type": "string"},
"email": {"type": "string"},
"phone": {"type": "string"},
"location": {"type": "string"},
"workExperience": {
"type": "array",
"items": {
"type": "object",
"properties": {
"jobTitle": {"type": "string"},
"employer": {"type": "string"},
"startDate": {"type": "string", "description": "YYYY-MM"},
"endDate": {"type": ["string", "null"], "description": "YYYY-MM, null if current"},
},
"required": ["jobTitle", "employer"],
},
},
"education": {
"type": "array",
"items": {
"type": "object",
"properties": {
"institution": {"type": "string"},
"qualification": {"type": "string"},
"year": {"type": ["integer", "null"]},
},
},
},
"skills": {"type": "array", "items": {"type": "string"}},
},
"required": ["fullName", "workExperience"],
}
response = requests.post(
"https://global.pdfvector.com/api/document/extract",
headers={
"Authorization": "Bearer YOUR_API_KEY",
"x-pdfvector-document-id": "candidate-4821",
},
json={
"url": "https://example.com/cv/jordan-nguyen.pdf",
"prompt": "Extract the candidate's contact details, every job with dates, education and skills. Use YYYY-MM for dates.",
"schema": schema,
"model": "mini",
},
timeout=300,
)
response.raise_for_status()
result = response.json()
print(result["data"]["fullName"], result["pageCount"], "pages,", result["credits"], "credits")
Cost. Document Extract costs 2, 4, 8 or 16 credits per page on nano, mini, pro and max. A 2-page CV on mini costs 2 × 4 = 8 credits, so the Basic plan's 3,000 credits cover 375 such CVs a month (3,000 ÷ 8). Word files are counted at 3,000 characters per page. A 12-page supplier invoice on Invoice Extract pro costs 12 × 14 = 168 credits.
Formats and limits. PDF, Word, Excel, PowerPoint, CSV, HTML, RTF, OpenDocument, EPUB and text on every tier; images on pro, max or auto. nano, mini and pro take up to 40 MB and 30 pages per file; max and auto up to 500 MB and 2,000 pages.
Beyond extraction. Document Parse returns Markdown for LLM and RAG pipelines, Ask answers questions about a single document, and the academic APIs search eight scholarly sources (Semantic Scholar, PubMed, arXiv, Google Scholar, ERIC, Europe PMC, OpenAlex and Crossref). Zapier, Make and n8n actions and an MCP server cover no-code and agent use.
Where Affinda is better. PDF Vector has no human review UI, no document splitting or classification step, no master-data matching and no ERP connectors; you build those in your code or automation tool. For resumes, it returns what your schema asks for, but it does not map job titles to O*NET or ISCO codes or normalize skills to a taxonomy the way Affinda's Resume Parser does. Self-hosting is not offered; Enterprise customers get dedicated managed instances in the UK, EU, US, Japan, Australia or Asia Pacific.
2. Mindee: prebuilt models plus custom extractors
Mindee offers prebuilt APIs for receipts, invoices, financial documents and international IDs, and lets you create custom extractors from a prompt without a training pipeline (mindee.com). Its platform lists a data extractor, classifier, splitter and edge detector, and official SDKs for Python, JavaScript, Java and more.
On the pricing page (checked November 2026), Starter starts at $53 per month billed annually and Pro at $134 per month billed annually, with credit volumes you choose; one processed page equals one credit (1.5 credits with confidence scores), unused credits do not carry over, and there is a 14-day free trial. Pro adds unlimited seats and data processing localization.
Choose Mindee if you want a developer API close to Affinda's extraction model, with confidence scores and polygons, at a published price.
3. Veryfi: finance documents and mobile capture
Veryfi focuses on financial documents. Its pricing page (checked November 2026) lists a free plan for up to 100 documents per month and a Starter plan with a $500 per month minimum commitment, with per-document prices of $0.16 per invoice, $0.08 per receipt, $0.25 per bank check, $0.25 per bank statement and $0.16 per W-2 or W-9 form. Mobile and browser capture SDKs and a fraud detection suite are available as add-ons, and the Growth plan adds volume discounts and model training.
Choose Veryfi when receipts and invoices dominate and you want capture inside a mobile app. It is not aimed at contracts, research or resumes.
4. Nanonets: workflow and review for operations teams
If what you valued in Affinda was the review queue and ERP export rather than the API, Nanonets is the closest match on this list. It combines workflow apps for accounts payable (approvals, review queues, exports to QuickBooks, Xero and other ERPs) with an extraction API that returns data in your schema, plus open-weight OCR models you can run yourself (pricing, checked November 2026). Pricing is per block run ($0.02, $0.10 or $0.30) rather than per page, with $50 of credits on Starter. Full comparison: PDF Vector vs Nanonets.
5. Azure Document Intelligence: enterprise cloud and containers
Microsoft's service provides Read OCR, a Layout model with markdown output, prebuilt models for invoices, receipts and IDs, and custom models you train on labeled documents. It runs in Azure regions and in Docker containers on premises. On the Azure pricing page (East US, checked November 2026), Read costs $1.50 per 1,000 pages, prebuilt and Layout $10 per 1,000 pages, custom extraction $30 per 1,000 pages, with 500 free pages per month on F0.
Choose it if you are already on Azure and want Microsoft's compliance programs and on-premises containers. Expect to set up a subscription and resource, and to train models for fields the prebuilt ones do not cover. See PDF Vector vs Azure Document Intelligence.
6. Eden AI: compare resume and invoice engines behind one API
Eden AI is an AI gateway: one API key and one bill for around 1,000 models from 80+ providers, including OCR and document parsers. Its Resume Parser docs (checked November 2026) list Affinda at $0.07 per file, Extracta at $0.10 per page, Klippa at $0.10 per file, OpenAI at $0.04 per page and Senseloaf at $0.045 per file. You pay the provider price plus a 5.5% platform fee, with no subscription (pricing).
That makes Eden AI useful mainly for comparison: you can benchmark several engines, including Affinda's, on your CVs and switch without rewriting the integration. Affinda's parser through Eden AI costs $0.07 per file plus the 5.5% fee (about $0.074), against US$0.10 per document on Affinda's own self-serve plan, which is also pay-as-you-go; at volume, Affinda's annual plans are cheaper still (US$3,600 ÷ 66,000 ≈ $0.055 per document). The output is Eden AI's fixed resume format, so you adapt your code to it rather than defining your own schema. More detail in PDF Vector vs Eden AI.
How to evaluate an Affinda alternative
Run the same test on every shortlisted tool:
- Build a test set of 30 to 50 real documents: clean PDFs, scans, phone photos, multi-page files and your hardest layouts. For resumes, include two-column designs, non-English CVs and DOCX files.
- Write the target output first. A JSON Schema with the exact fields and formats you need (the free JSON Schema Editor helps). For fixed-format tools, map their output to it.
- Score field by field. Count exact matches per field, not per document. A tool that misses only phone numbers is easier to fix than one that misses dates of employment.
- Measure cost on your page counts. Per-file and per-page pricing diverge quickly on long documents.
- Check the operational parts you need: review UI, data region, compliance documents, integrations with your ATS, ERP or automation tool.
Which one to choose
- Self-serve API, your own schema, many document types, published credits: PDF Vector.
- Prebuilt models plus custom extractors at a published price: Mindee.
- Receipts, invoices and mobile capture: Veryfi.
- Approvals, review queues and ERP exports for an ops team: Nanonets.
- Azure shop or on-premises containers: Azure Document Intelligence.
- Resume parsing with a choice of engines: Eden AI.
- Regulated, multi-document workflows that need validation, evidence on every field, human review and self-hosting: stay with Affinda Extract. Few alternatives cover that whole path.
For a feature-by-feature view of the two products, read PDF Vector vs Affinda.
